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"""HunYuanMoEV1 model configuration"""

from typing import Optional, Union

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
from ...utils import logging


logger = logging.get_logger(__name__)


class HunYuanMoEV1Config(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`HunYuanMoEV1Model`]. It is used to instantiate an
    HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the HunYuan-7B.
    Hunyuan-A13B-Instruct [tencent/Hunyuan-A13B-Instruct](https://huggingface.co/tencent/Hunyuan-A13B-Instruct).

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 290943):
            Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`HunYuanMoEV1Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations or shared MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        eod_token_id (int, *optional*, defaults to 3):
            Token ID representing the end-of-document marker. Used to indicate the termination of a text sequence.
            Example: In multi-document processing, this token helps the model distinguish between separate documents.
        sep_token_id (`int`, *optional*, defaults to 4):
            Token ID representing the separator token (`[SEP]`), used to demarcate boundaries between different text segments.
        pretraining_tp (`int`, *optional*, defaults to 1):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
            issue](https://github.com/pytorch/pytorch/issues/76232).
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        num_experts (`int` or `List`, *optional*, defaults to 1):
            The number of experts for moe. If it is a list, it will be used as the number of experts for each layer.
        moe_topk (int or List, *optional*, defaults to 1):
            Number of experts selected per token (Top-K routing). List form enables layer-wise customization.
        head_dim (`int`, *optional*, defaults to 128):
            The attention head dimension.
    """

    model_type = "hunyuan_v1_moe"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_experts_per_tok": "moe_topk",
        "num_local_experts": "num_experts",
    }

    def __init__(
        self,
        vocab_size: Optional[int] = 290943,
        hidden_size: Optional[int] = 4096,
        intermediate_size: Optional[int] = 11008,
        num_hidden_layers: Optional[int] = 32,
        num_attention_heads: Optional[int] = 32,
        num_key_value_heads: Optional[int] = None,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 2048,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[float] = 1e-5,
        use_cache: Optional[bool] = True,
        pad_token_id: Optional[int] = 0,
        bos_token_id: Optional[int] = 1,
        eos_token_id: Optional[int] = 2,
        eod_token_id: Optional[int] = 3,
        sep_token_id: Optional[int] = 4,
        pretraining_tp: Optional[int] = 1,
        tie_word_embeddings: Optional[bool] = False,
        rope_parameters: Optional[RopeParameters | dict[str, RopeParameters]] = None,
        attention_bias: Optional[bool] = False,
        attention_dropout: Optional[float] = 0.0,
        num_experts: Union[int, list] = 1,
        moe_topk: Union[int, list] = 1,
        head_dim: Optional[int] = None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_experts = num_experts
        self.moe_topk = moe_topk

        self.head_dim = head_dim
        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or rope_parameters

        # Validate the correctness of rotary position embeddings parameters
        rope_theta = kwargs.get("rope_theta", 10000.0)
        standardize_rope_params(self, rope_theta=rope_theta)
        rope_config_validation(self)

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            sep_token_id=sep_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_parameters_validation(self):
        """
        Validate the `rope_parameters` configuration.
        """
        if self.rope_parameters is None:
            return

        if not isinstance(self.rope_parameters, dict) or len(self.rope_parameters) != 2:
            raise ValueError(
                "`rope_parameters` must be a dictionary with with two fields, `type` and `factor` or `type` and `alpha`, "
                f"got {self.rope_parameters}"
            )
        rope_parameters_type = self.rope_parameters.get("type", None)
        rope_parameters_factor = self.rope_parameters.get("factor", None)
        rope_parameters_alpha = self.rope_parameters.get("alpha", None)
        if rope_parameters_type is None or rope_parameters_type not in ["linear", "dynamic"]:
            raise ValueError(
                f"`rope_parameters`'s type field must be one of ['linear', 'dynamic'], got {rope_parameters_type}"
            )
        if rope_parameters_factor is None and rope_parameters_alpha is None:
            raise ValueError("`rope_parameters`'s factor or alpha field must be have one, got both of none")
        if rope_parameters_factor is not None:
            if not isinstance(rope_parameters_factor, float) or rope_parameters_factor <= 1.0:
                raise ValueError(
                    f"`rope_parameters`'s factor field must be a float > 1.0, got {rope_parameters_factor}"
                )
        if rope_parameters_alpha is not None:
            if not isinstance(rope_parameters_alpha, float) or rope_parameters_alpha <= 1.0:
                raise ValueError(f"`rope_parameters`'s alpha field must be a float > 1.0, got {rope_parameters_alpha}")


__all__ = ["HunYuanMoEV1Config"]
